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Local maximum likelihood estimation and inference
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AI
DOI：10.1111/1467-9868.00142.png)
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Abstract
En 中文
Local maximum likelihood estimation is a nonparametric counterpart of the widely used parametric maximum likelihood technique. It extends the scope of the parametric maximum likelihood method to a much wider class of parametric spaces. Associated with this nonparametric estimation scheme is the issue of bandwidth selection and bias and variance assessment. This paper provides a unified approach to selecting a bandwidth and constructing confidence intervals in local maximum likelihood estimation. The approach is then applied to least squares nonparametric regression and to nonparametric logistic regression. Our experiences in these two settings show that the general idea outlined here is powerful and encouraging.
Keywords:
bandwidth selection
confidence intervals
generalized linear models
logit regression
maximum likelihood
nonparametric regression
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
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